A research publication is accepted quickly by the publisher of a scientific journal if the statistical analysis have been incorporated by the authors. On the other hand , a slight increase or decrease in grain yield obtained has a considerable effect on the farmers income, however , difference in yield may or may not be significant. Hence, Is statistical analysis only relevant to the scientific community and not to the farmers?
In such cases , the researcher can resort to more descriptive, qualitative methods which require an entirely different statistical treatment techniques called non-parametric tests.
A small increase in grain yield can increase farm income because there is more product, or cause a glut in the market and reduce prices and farm income.
A grower that sees a research result that is not significant but numerically larger has two choices. They could assume that the effect is highly significant but the scientist did not have sufficient replication, or the increase in the scientific study is actually zero and if they try the same treatment in their field they have a 50-50 chance of getting a better or worse yield. To achieve this 50-50 chance they must pay $/hectare for the miracle cure.
Without some ability to interpret scientific research the grower is at the mercy of corporate, university, and governmental agencies that each may have their own agendas.
Dear Manoj Sharma ,
Statistical analysis is only a decision-making tool. In other words, when the farmer sows and gets a given increase in production, is it not just a random result? Or can't that be considered just by chance that may not happen again? It is to this kind of question that statistics allow us to give answers, with a certain uncertainty, of course. This said, the farmer also needs to carry out statistical analyses to be able to estimate the increases (or decreases) in production that he is likely to achieve in order to better secure his investment.
An example would be a farmer who wants to know if using a certain input really increases his production. In this case, the statistical tools can be used to compare the quantity of production obtained using that input with that obtained without its use, and thus determine whether the differences obtained are just random result of chance or whether they can actually be attributed to the use of the new input.
The scientific communities these days have the tradition of accepting research articles with different or difficult statistical models which according to me is not a good thing as these papers are not understandable by majority of the readers. There are so many policy issues related to agriculture which do not require much statistical analysis. These types of papers need to be considered which can be useful to the readers..
Regarding the slight increase or slight decrease in the grain yield, definitely it will have effect on income if that particular farmer, i.e. at micro level. When aggregated at macro level, it this effect may be negligible i.e. statistical non-significant. You can use different levels of significance for checking the significance of grain yield increase i.e. 1%, 5%, 10%. and interpret the results accordingly.
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